Overview

Dataset statistics

Number of variables13
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.1 KiB
Average record size in memory105.5 B

Variable types

Numeric11
Categorical2

Alerts

Temperature is highly correlated with RH and 6 other fieldsHigh correlation
RH is highly correlated with Temperature and 4 other fieldsHigh correlation
Rain is highly correlated with FFMC and 6 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 8 other fieldsHigh correlation
DMC is highly correlated with Temperature and 8 other fieldsHigh correlation
DC is highly correlated with Rain and 6 other fieldsHigh correlation
ISI is highly correlated with Temperature and 8 other fieldsHigh correlation
BUI is highly correlated with Temperature and 7 other fieldsHigh correlation
FWI is highly correlated with Temperature and 8 other fieldsHigh correlation
Classes is highly correlated with Temperature and 7 other fieldsHigh correlation
Temperature is highly correlated with RH and 4 other fieldsHigh correlation
RH is highly correlated with Temperature and 3 other fieldsHigh correlation
Rain is highly correlated with FFMCHigh correlation
FFMC is highly correlated with Temperature and 8 other fieldsHigh correlation
DMC is highly correlated with FFMC and 5 other fieldsHigh correlation
DC is highly correlated with FFMC and 5 other fieldsHigh correlation
ISI is highly correlated with Temperature and 7 other fieldsHigh correlation
BUI is highly correlated with FFMC and 5 other fieldsHigh correlation
FWI is highly correlated with Temperature and 7 other fieldsHigh correlation
Classes is highly correlated with Temperature and 6 other fieldsHigh correlation
Temperature is highly correlated with FFMCHigh correlation
RH is highly correlated with FFMCHigh correlation
Rain is highly correlated with FFMC and 3 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 8 other fieldsHigh correlation
DMC is highly correlated with FFMC and 5 other fieldsHigh correlation
DC is highly correlated with FFMC and 5 other fieldsHigh correlation
ISI is highly correlated with Rain and 6 other fieldsHigh correlation
BUI is highly correlated with FFMC and 5 other fieldsHigh correlation
FWI is highly correlated with Rain and 6 other fieldsHigh correlation
Classes is highly correlated with Rain and 6 other fieldsHigh correlation
df_index is highly correlated with month and 7 other fieldsHigh correlation
month is highly correlated with df_index and 4 other fieldsHigh correlation
Temperature is highly correlated with df_index and 8 other fieldsHigh correlation
RH is highly correlated with df_index and 5 other fieldsHigh correlation
Ws is highly correlated with Temperature and 1 other fieldsHigh correlation
Rain is highly correlated with Temperature and 2 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 7 other fieldsHigh correlation
DMC is highly correlated with df_index and 7 other fieldsHigh correlation
DC is highly correlated with df_index and 5 other fieldsHigh correlation
ISI is highly correlated with df_index and 8 other fieldsHigh correlation
BUI is highly correlated with df_index and 7 other fieldsHigh correlation
FWI is highly correlated with df_index and 8 other fieldsHigh correlation
Classes is highly correlated with month and 8 other fieldsHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique
Rain has 133 (54.5%) zeros Zeros
ISI has 4 (1.6%) zeros Zeros
FWI has 9 (3.7%) zeros Zeros

Reproduction

Analysis started2022-06-05 03:04:27.471317
Analysis finished2022-06-05 03:04:38.085009
Duration10.61 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct244
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.5
Minimum0
Maximum245
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:38.147842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.15
Q160.75
median122.5
Q3184.25
95-th percentile232.85
Maximum245
Range245
Interquartile range (IQR)123.5

Descriptive statistics

Standard deviation71.45049223
Coefficient of variation (CV)0.5832693243
Kurtosis-1.214936047
Mean122.5
Median Absolute Deviation (MAD)62
Skewness0
Sum29890
Variance5105.17284
MonotonicityStrictly increasing
2022-06-05T08:34:38.261198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.4%
1551
 
0.4%
1571
 
0.4%
1581
 
0.4%
1591
 
0.4%
1601
 
0.4%
1611
 
0.4%
1621
 
0.4%
1631
 
0.4%
1641
 
0.4%
Other values (234)234
95.9%
ValueCountFrequency (%)
01
0.4%
11
0.4%
21
0.4%
31
0.4%
41
0.4%
51
0.4%
61
0.4%
71
0.4%
81
0.4%
91
0.4%
ValueCountFrequency (%)
2451
0.4%
2441
0.4%
2431
0.4%
2421
0.4%
2411
0.4%
2401
0.4%
2391
0.4%
2381
0.4%
2371
0.4%
2361
0.4%

month
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
7
62 
8
62 
6
60 
9
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Length

2022-06-05T08:34:38.347937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-05T08:34:38.421772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring characters

ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number244
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
762
25.4%
862
25.4%
660
24.6%
960
24.6%

Temperature
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.17213115
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:38.489207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.85
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.63384326
Coefficient of variation (CV)0.1129500325
Kurtosis-0.1543103757
Mean32.17213115
Median Absolute Deviation (MAD)3
Skewness-0.1963088795
Sum7850
Variance13.20481684
MonotonicityNot monotonic
2022-06-05T08:34:38.565001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3529
11.9%
3125
10.2%
3424
9.8%
3323
9.4%
3022
9.0%
3221
8.6%
3621
8.6%
2918
7.4%
2815
6.1%
379
 
3.7%
Other values (9)37
15.2%
ValueCountFrequency (%)
222
 
0.8%
243
 
1.2%
256
 
2.5%
265
 
2.0%
278
 
3.3%
2815
6.1%
2918
7.4%
3022
9.0%
3125
10.2%
3221
8.6%
ValueCountFrequency (%)
421
 
0.4%
403
 
1.2%
396
 
2.5%
383
 
1.2%
379
 
3.7%
3621
8.6%
3529
11.9%
3424
9.8%
3323
9.4%
3221
8.6%

RH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.93852459
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:38.670844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152
median63
Q373.25
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation14.88420018
Coefficient of variation (CV)0.2403060176
Kurtosis-0.5303278714
Mean61.93852459
Median Absolute Deviation (MAD)11
Skewness-0.2379643933
Sum15113
Variance221.5394151
MonotonicityNot monotonic
2022-06-05T08:34:38.777559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410
 
4.1%
5510
 
4.1%
588
 
3.3%
548
 
3.3%
788
 
3.3%
687
 
2.9%
667
 
2.9%
737
 
2.9%
807
 
2.9%
657
 
2.9%
Other values (52)165
67.6%
ValueCountFrequency (%)
211
 
0.4%
241
 
0.4%
261
 
0.4%
291
 
0.4%
311
 
0.4%
332
0.8%
343
1.2%
351
 
0.4%
361
 
0.4%
374
1.6%
ValueCountFrequency (%)
901
 
0.4%
893
1.2%
883
1.2%
874
1.6%
863
1.2%
842
 
0.8%
831
 
0.4%
823
1.2%
816
2.5%
807
2.9%

Ws
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.50409836
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:38.866321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.810178371
Coefficient of variation (CV)0.181253905
Kurtosis2.602155825
Mean15.50409836
Median Absolute Deviation (MAD)2
Skewness0.5458812499
Sum3783
Variance7.897102476
MonotonicityNot monotonic
2022-06-05T08:34:38.945111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1443
17.6%
1540
16.4%
1330
12.3%
1728
11.5%
1627
11.1%
1826
10.7%
1915
 
6.1%
218
 
3.3%
117
 
2.9%
127
 
2.9%
Other values (8)13
 
5.3%
ValueCountFrequency (%)
61
 
0.4%
81
 
0.4%
92
 
0.8%
103
 
1.2%
117
 
2.9%
127
 
2.9%
1330
12.3%
1443
17.6%
1540
16.4%
1627
11.1%
ValueCountFrequency (%)
291
 
0.4%
261
 
0.4%
222
 
0.8%
218
 
3.3%
202
 
0.8%
1915
 
6.1%
1826
10.7%
1728
11.5%
1627
11.1%
1540
16.4%

Rain
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7606557377
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:39.032439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.355
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.999405565
Coefficient of variation (CV)2.628528868
Kurtosis25.94212272
Mean0.7606557377
Median Absolute Deviation (MAD)0
Skewness4.579070596
Sum185.6
Variance3.997622614
MonotonicityNot monotonic
2022-06-05T08:34:39.122599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0133
54.5%
0.118
 
7.4%
0.212
 
4.9%
0.310
 
4.1%
0.48
 
3.3%
0.76
 
2.5%
0.66
 
2.5%
0.55
 
2.0%
1.13
 
1.2%
1.23
 
1.2%
Other values (29)40
 
16.4%
ValueCountFrequency (%)
0133
54.5%
0.118
 
7.4%
0.212
 
4.9%
0.310
 
4.1%
0.48
 
3.3%
0.55
 
2.0%
0.66
 
2.5%
0.76
 
2.5%
0.82
 
0.8%
0.91
 
0.4%
ValueCountFrequency (%)
16.81
0.4%
13.11
0.4%
10.11
0.4%
8.71
0.4%
8.31
0.4%
7.21
0.4%
6.51
0.4%
61
0.4%
5.81
0.4%
4.71
0.4%

FFMC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.88770492
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:39.226324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.145
Q172.075
median83.5
Q388.3
95-th percentile92.185
Maximum96
Range67.4
Interquartile range (IQR)16.225

Descriptive statistics

Standard deviation14.33757088
Coefficient of variation (CV)0.1840800277
Kurtosis1.05520829
Mean77.88770492
Median Absolute Deviation (MAD)5.7
Skewness-1.325633262
Sum19004.6
Variance205.5659387
MonotonicityNot monotonic
2022-06-05T08:34:39.321098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.98
 
3.3%
89.45
 
2.0%
89.34
 
1.6%
85.44
 
1.6%
89.14
 
1.6%
78.33
 
1.2%
88.13
 
1.2%
88.33
 
1.2%
47.43
 
1.2%
79.93
 
1.2%
Other values (163)204
83.6%
ValueCountFrequency (%)
28.61
0.4%
30.51
0.4%
36.11
0.4%
37.31
0.4%
37.91
0.4%
40.91
0.4%
41.11
0.4%
42.61
0.4%
44.91
0.4%
451
0.4%
ValueCountFrequency (%)
961
0.4%
94.31
0.4%
94.21
0.4%
93.92
0.8%
93.81
0.4%
93.71
0.4%
93.31
0.4%
931
0.4%
92.52
0.8%
92.22
0.8%

DMC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct166
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.67336066
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:39.424841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.75
95-th percentile41.01
Maximum65.9
Range65.2
Interquartile range (IQR)14.95

Descriptive statistics

Standard deviation12.36803873
Coefficient of variation (CV)0.8428906658
Kurtosis2.487598085
Mean14.67336066
Median Absolute Deviation (MAD)6.9
Skewness1.527652386
Sum3580.3
Variance152.9683821
MonotonicityNot monotonic
2022-06-05T08:34:39.523578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95
 
2.0%
12.54
 
1.6%
1.94
 
1.6%
3.43
 
1.2%
4.63
 
1.2%
163
 
1.2%
63
 
1.2%
3.23
 
1.2%
9.73
 
1.2%
2.63
 
1.2%
Other values (156)210
86.1%
ValueCountFrequency (%)
0.71
 
0.4%
0.92
0.8%
1.12
0.8%
1.21
 
0.4%
1.33
1.2%
1.71
 
0.4%
1.94
1.6%
2.11
 
0.4%
2.22
0.8%
2.41
 
0.4%
ValueCountFrequency (%)
65.91
0.4%
61.31
0.4%
56.31
0.4%
54.21
0.4%
51.31
0.4%
50.21
0.4%
471
0.4%
46.61
0.4%
46.11
0.4%
45.61
0.4%

DC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct198
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.28811475
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:39.633125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q113.275
median33.1
Q368.15
95-th percentile158.86
Maximum220.4
Range213.5
Interquartile range (IQR)54.875

Descriptive statistics

Standard deviation47.61966238
Coefficient of variation (CV)0.9661489918
Kurtosis1.614097336
Mean49.28811475
Median Absolute Deviation (MAD)23.9
Skewness1.479041666
Sum12026.3
Variance2267.632245
MonotonicityNot monotonic
2022-06-05T08:34:39.729097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85
 
2.0%
7.64
 
1.6%
7.84
 
1.6%
8.44
 
1.6%
7.54
 
1.6%
8.34
 
1.6%
8.24
 
1.6%
173
 
1.2%
16.62
 
0.8%
102
 
0.8%
Other values (188)208
85.2%
ValueCountFrequency (%)
6.91
 
0.4%
72
0.8%
7.11
 
0.4%
7.32
0.8%
7.42
0.8%
7.54
1.6%
7.64
1.6%
7.72
0.8%
7.84
1.6%
7.91
 
0.4%
ValueCountFrequency (%)
220.41
0.4%
210.41
0.4%
200.21
0.4%
190.61
0.4%
181.31
0.4%
180.41
0.4%
177.31
0.4%
171.31
0.4%
168.21
0.4%
167.21
0.4%

ISI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct106
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.759836066
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:39.834007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.3
95-th percentile13.37
Maximum19
Range19
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.15462774
Coefficient of variation (CV)0.8728510148
Kurtosis0.8298604263
Mean4.759836066
Median Absolute Deviation (MAD)2.4
Skewness1.126950083
Sum1161.4
Variance17.26093166
MonotonicityNot monotonic
2022-06-05T08:34:39.935736image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.18
 
3.3%
1.27
 
2.9%
5.25
 
2.0%
1.55
 
2.0%
2.85
 
2.0%
4.75
 
2.0%
5.65
 
2.0%
0.45
 
2.0%
15
 
2.0%
1.44
 
1.6%
Other values (96)190
77.9%
ValueCountFrequency (%)
04
1.6%
0.14
1.6%
0.24
1.6%
0.33
1.2%
0.45
2.0%
0.52
 
0.8%
0.64
1.6%
0.74
1.6%
0.83
1.2%
0.92
 
0.8%
ValueCountFrequency (%)
191
0.4%
18.51
0.4%
17.21
0.4%
16.61
0.4%
161
0.4%
15.72
0.8%
15.51
0.4%
14.31
0.4%
14.21
0.4%
13.82
0.8%

BUI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.67336066
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:40.094313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.43
Q16
median12.45
Q322.525
95-th percentile46.35
Maximum68
Range66.9
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation14.20164844
Coefficient of variation (CV)0.8517568071
Kurtosis1.979913218
Mean16.67336066
Median Absolute Deviation (MAD)7.35
Skewness1.458466015
Sum4068.3
Variance201.6868183
MonotonicityNot monotonic
2022-06-05T08:34:40.222969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35
 
2.0%
5.14
 
1.6%
14.23
 
1.2%
2.93
 
1.2%
11.53
 
1.2%
8.33
 
1.2%
2.43
 
1.2%
7.73
 
1.2%
14.13
 
1.2%
4.43
 
1.2%
Other values (163)211
86.5%
ValueCountFrequency (%)
1.11
 
0.4%
1.42
0.8%
1.62
0.8%
1.72
0.8%
1.82
0.8%
2.21
 
0.4%
2.43
1.2%
2.62
0.8%
2.72
0.8%
2.82
0.8%
ValueCountFrequency (%)
681
0.4%
67.41
0.4%
641
0.4%
62.91
0.4%
59.51
0.4%
59.31
0.4%
57.11
0.4%
54.91
0.4%
54.71
0.4%
50.91
0.4%

FWI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct126
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.049180328
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2022-06-05T08:34:40.334698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.45
Q311.375
95-th percentile21.495
Maximum31.1
Range31.1
Interquartile range (IQR)10.675

Descriptive statistics

Standard deviation7.428365676
Coefficient of variation (CV)1.05379141
Kurtosis0.6553162089
Mean7.049180328
Median Absolute Deviation (MAD)4.05
Skewness1.143242624
Sum1720
Variance55.18061661
MonotonicityNot monotonic
2022-06-05T08:34:40.448366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
0.19
 
3.7%
09
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.0%
0.64
 
1.6%
Other values (116)165
67.6%
ValueCountFrequency (%)
09
3.7%
0.19
3.7%
0.26
2.5%
0.38
3.3%
0.412
4.9%
0.59
3.7%
0.64
 
1.6%
0.75
2.0%
0.810
4.1%
0.97
2.9%
ValueCountFrequency (%)
31.11
0.4%
30.31
0.4%
30.21
0.4%
301
0.4%
26.91
0.4%
26.31
0.4%
26.11
0.4%
25.41
0.4%
24.51
0.4%
241
0.4%

Classes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
138 
1
106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters244
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Length

2022-06-05T08:34:40.569044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-05T08:34:40.678780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Most occurring characters

ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number244
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Most occurring scripts

ValueCountFrequency (%)
Common244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0138
56.6%
1106
43.4%

Interactions

2022-06-05T08:34:36.811419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:27.733588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.686087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.619571image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.809015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.609536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.466227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.298567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.277712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.088157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.973690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.882680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:27.831326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.765860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.692376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.880823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.681655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.538035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.378327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.345367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.161382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.048460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.965458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:27.918094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.863566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.778118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.959612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.764434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.621811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.464096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.423157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.250137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.131266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.055220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.003865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.958311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.855947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.033414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.843895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.696610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.540890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.501952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.335935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.208064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.124068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.098611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.042087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.927757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.101896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.918692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.768418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.610733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.571760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.408713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.278843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.199863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.184381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.128855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.007538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.177693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.997480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.844782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.828151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.651392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.491492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.357633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.273943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.275139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.217619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.093275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.251496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.077267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.923542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.901953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.720237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.578259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.437447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.345753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.358915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.299399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.169074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.322305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.157055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.001333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.975727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.797904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.660040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.513217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.415634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.428727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.374229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.241402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.389126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.228866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.076136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.044543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.864726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.732875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.584056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.645020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.505523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.457010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.319198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.463927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.309618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.151931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.123154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.942519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.817620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.662817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:37.719820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:28.612237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:29.539786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:30.737235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:31.538700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:32.387438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:33.226763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:34.203909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.019312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:35.894870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-05T08:34:36.739641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-05T08:34:40.743604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-05T08:34:40.928084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-05T08:34:41.059731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-05T08:34:41.179412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-05T08:34:41.260195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-05T08:34:37.856773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-05T08:34:38.028553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexmonthTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
0062957180.065.73.47.61.33.40.51
1162961131.364.44.17.61.03.90.41
22626822213.147.12.57.10.32.70.11
3362589132.528.61.36.90.01.70.01
4462777160.064.83.014.21.23.90.51
5563167140.082.65.822.23.17.02.50
6663354130.088.29.930.56.410.97.20
7763073150.086.612.138.35.613.57.10
8862588130.252.97.938.80.410.50.31
9962879120.073.29.546.31.312.60.91

Last rows

df_indexmonthTemperatureRHWsRainFFMCDMCDCISIBUIFWIClasses
23423693534170.092.223.697.313.829.421.60
23523793364130.088.926.1106.37.132.413.70
23623893556140.089.029.4115.67.536.015.20
2372399264962.061.311.928.10.611.90.41
23824092870150.079.913.836.12.414.13.01
23924193065140.085.416.044.54.516.96.50
24024292887154.441.16.58.00.16.20.01
24124392787290.545.93.57.90.43.40.21
24224492454180.179.74.315.21.75.10.71
24324592464150.267.33.816.51.24.80.51